What you should know about approximate dynamic programming

Approximate dynamic programming (ADP) is a broad umbrella for a modeling and algorithmic strategy for solving problems that are sometimes large and complex, and are usually (but not always) stochastic. It is most often presented as a method for overcoming the classic curse of dimensionality that is well‐known to plague the use of Bellman's equation. For many problems, there are actually up to three curses of dimensionality. But the richer message of approximate dynamic programming is learning what to learn, and how to learn it, to make better decisions over time. This article provides a brief review of approximate dynamic programming, without intending to be a complete tutorial. Instead, our goal is to provide a broader perspective of ADP and how it should be approached from the perspective of different problem classes. © 2009 Wiley Periodicals, Inc. Naval Research Logistics 2009     

Two‐machine flow shop scheduling with common due window to minimize weighted number of early and tardy jobs

This article studies two due window scheduling problems to minimize the weighted number of early and tardy jobs in a two‐machine flow shop, where the window size is externally determined. These new scheduling models have many practical applications in real life. However, results on these problems have rarely appeared in the literature because of a lack of structural and optimality properties for solving them. In this article, we derive several dominance properties and theorems, including elimination rules and sequencing rules based on Johnsos order, lower bounds on the penalty, and upper bounds on the window location, which help to significantly trim the search space for the problems. We further show that the problems are NP‐hard in the ordinary sense only. We finally develop efficient pseudopolynomial dynamic programming algorithms for solving the problems. © 2009 Wiley Periodicals, Inc. Naval Research Logistics, 2009     

扭力矩对曲折槽保险机构动力学响应影响分析

在考虑扭力矩作用的基础上,建立曲折槽保险机构的动力学模型,以某机构为计算实例,与传统的动力学模型进行比较。数值计算结果表明：引信跌落到硬质地面时,扭力矩的影响作用不明显,而跌落到松软地面上时扭力矩使惯性简的位移响应明显增大,不能忽略。其结论为判断长储引信勤务处理意外跌落的安全状态提供了理论参考。     

软件保障费用估算模型研究

分析了软件保障费用的基本构成和一般表达式,构建了软件错误更改费用估算模型、新功能保障费用估算模型和有时间限制的保障费用估算模型,并结合实例进行了分析。软件保障模型估算的研究对提高软件保障费用估算的精度和效率有重要意义。     

农村经济结构调整的动力机制分析

农村经济结构调整是一系列因素共同作用的结果 ,引致这种调整的动力机制包括政策导向动力机制、市场需求动力机制、科技发展动力机制和国际竞争动力机制。这四大动力机制共同作用 ,使农村经济结构的调整朝着符合国内外需求、优质高效的目标发展。     

垂直惯性基准零位安装误差的校正模型

通过空间矢量法 ,推导出在舰载武器系统中由于垂直惯性基准零位安装误差所引入的坐标变换误差的解析式 ,并给出了误差校正公式     

自适应数据库应用系统的开发方法与实现

在数据库应用系统开发过程中 ,往往需要考虑系统的自适应性。介绍用PowerBuilder实现动态修改表结构、数据处理界面和打印表格格式的方法 ,从而达到使系统具有自适应性的目的。     

浅谈篮球教学中错误动作的纠正方法

篮球教学中,教师对学生在学习技、战术过程中所产生的错误,应及时、果断地采取有针对性的纠正方法.本文根据不同教学对象,不同技战术特点,及篮球教学中各因素间的关系,从纠正错误的途径、措施和手段等方面着手,对篮球教学中的错误纠正方法做了一个概括.     

作战油料需求预计不确定性因素及误差分析

当前各种油料需求预计单纯地追求表面精确而忽视实有误差,重静止轻动态,脱离了作战保障实际。从技术、勤务和战术结合的层面,将油料需求预计不确定性因素归结为油料消耗标准本身、计划与实际运用差异、影响油耗的自然因素考虑不同、作战任务理解判断差别四点,并对各自的误差范围进行了粗略分析。提出的不确定性因素及误差范围界定,有助于引发人们对作战油料需求预计新的思考,提高油料勤务理论研究和实践能力水平。     

Dynamic regulation on innovation and adoption of green technology with information asymmetry

This study investigates a regulator's dynamic policy to motivate firms' research on and adoption of green technology. In the proposed model, a firm makes unobservable efforts and can hide the technology's arrival from the regulator to avoid adoption costs. We find that the optimal policy follows a simple structure and induces part-time efforts, rather than the maximal effort reported in previous studies. In particular, the regulator should offer no subsidy before the arrival of a technology report, provide a one-time subsidy contingent upon that report, and always set a termination deadline. At the deadline, the firm is forced to select an external option that is associated with social costs. The optimal report-based subsidy decreases with time. Under the optimal policy, the firm works until an effort deadline, makes no effort thereafter, and reports the technology as soon as it arrives. This study also characterizes the necessary and sufficient conditions under which the optimal policy reduces to one that leads, in terms of throughout time, to effort that is maximal or minimal. Our results indicate that policymakers should implement a policy that compensates firms more in the present and less in the future.